Systems and methods for sorting t cells by activation state

ABSTRACT

Systems and methods for sorting T cells are disclosed. Autofluorescence data is acquired from individual cells. An activation value is computed using one or more autofluorescence endpoints as an input. The one or more autofluorescence endpoints includes NAD(P)H shortest fluorescence lifetime amplitude component (α 1 ).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/724,428, filed Aug. 29, 2018, which is incorporatedby reference herein in its entirety for all purposes.

STATEMENT REGARDING FEDERALLY FUNDED RESEARCH

This invention was made with government support under CA205101 awardedby the National Institutes of Health. The government has certain rightsin the invention.

BACKGROUND

One new cancer treatment being studied is CAR T cell (Chimeric AntigenReceptor T cell) therapy. CAR T cell therapy uses a patient's own cellsand “re-engineers” them to fight cancer. It is a very complex treatment.Collecting and altering the cells is difficult, and CAR T cell therapyoften causes very severe side effects. At this time, it is only offeredat a few major cancer centers. To date, most of the patients treatedwith CAR T cell have been people with blood cancers.

The procedure starts with removing the patient's own T cells from theirblood and sending them to a lab where they are altered to produceproteins called chimeric antigen receptors (CARs) on the surface of thecells. These special receptors allow the T cells to help identify andattack cancer cells. The “super-charged” T cells are multiplied andgrown at the lab, then frozen and shipped back to Hospital, where theyre-inject these treated CAR T cells back into the patient's blood.

Current methods to determine T cell activation include flow cytometry,immunofluorescence imaging, and immunohistochemistry but these methodsrequire contrast agents and may require tissue or cell fixation. A needexists for system and methods for sorting T cells by activation state ina fashion that allows the sorted T cells to be used in subsequentprocedures, such as CAR T cell therapy.

SUMMARY

In one aspect, the present disclosure provides a T cell sorting device.The T cell sorting device includes a cell sorting pathway, a single-cellautofluorescence detector, a processor, and a non-transitorycomputer-readable medium. The cell sorting pathway includes an inlet, anobservation zone, and a cell sorter. The observation zone is coupled tothe inlet downstream of the inlet. The observation zone is configured topresent T cells for individual autofluorescence interrogation. The cellsorter has a sorter inlet and at least two sorter outlets. The cellsorter is coupled to the observation zone via the sorter inletdownstream of the observation zone. The cell sorter is configured toselectively direct a cell from the sorter inlet to one of the at leasttwo sorter outlets based on a sorter signal. The single-cellautofluorescence detector is configured to acquire an autofluorescencedata set from a T cell positioned in the observation zone. Thesingle-cell autofluorescence detector is configured to acquireautofluorescence lifetime information for each autofluorescence dataset. The processor is in electronic communication with the cell sorterand the single-cell autofluorescence detector. The non-transitorycomputer-readable medium has stored thereon instructions that, whenexecuted by the processor, cause the processor to: a) receive theautofluorescence data set; and b) provide the sorter signal to the cellsorter based on an activation value. The sorter signal directs the cellsorter to selectively direct the T cell to a first outlet of the atleast two sorter outlets when the activation value exceeds apredetermined threshold and to a second outlet of the at least twosorter outlets when the activation value is less than or equal to thepredetermined threshold. The activation value is computed using at leastone metabolic endpoint of the autofluorescence data set for the T cellas an input. The at least one metabolic endpoint includes reducednicotinamide adenine dinucleotide (phosphate) (NAD(P)H) shortestautofluorescence lifetime amplitude component (α₁).

In another aspect, the present disclosure provides a method of sorting Tcells. The method includes: a) receiving a population of T cells havingunknown activation status; b) acquiring an autofluorescence data set foreach T cell of the population of T cells, each autofluorescence data setincluding autofluorescence lifetime information; and c) physicallyisolating a first portion of the population of T cells from a secondportion of the population of T cells based on an activation value,wherein each T cell of the population of T cells is placed into thefirst portion when the activation value exceeds a predeterminedthreshold and into the second portion when the activation value is lessthan or equal to the predetermined threshold, wherein the activationvalue is computed using at least one metabolic endpoint of theautofluorescence data set for each T cell of the population of T cellsas an input, wherein the at least one metabolic endpoint includesNAD(P)H α₁.

In a further aspect, the present disclosure provides a method ofadministering activated T cells to a subject in need thereof. The methodincludes: physical isolating a first portion of a population of T cellsfrom a second portion of the population of T cells based on anactivation value, wherein each T cell of the population of T cells isplaced into the first portion when the activation value exceeds apredetermined threshold and into the second portion when the activationvalue is less than or equal to the predetermined threshold, wherein theactivation value is computed using at least one metabolic endpoint of anautofluorescence data set for each T cell of the population of T cellsas an input, wherein the at least one metabolic endpoint includesNAD(P)H α₁; and introducing the first portion of the population of Tcells to the subject. The method can further include modifying the firstportion of the population of T cells prior to the introducing to thesubject. The modification can be modification to include a chimericantigen receptor.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIG. 1 is a flowchart illustrating a method, in accordance with anaspect of the present disclosure.

FIG. 2 is a flowchart illustrating a method, in accordance with anaspect of the present disclosure.

FIG. 3 is a block diagram of a device, in accordance with an aspect ofthe present disclosure.

DETAILED DESCRIPTION

Before the present invention is described in further detail, it is to beunderstood that the invention is not limited to the particularembodiments described. It is also understood that the terminology usedherein is for the purpose of describing particular embodiments only, andis not intended to be limiting. The scope of the present invention willbe limited only by the claims. As used herein, the singular forms “a”,“an”, and “the” include plural embodiments unless the context clearlydictates otherwise.

Specific structures, devices and methods relating to modifyingbiological molecules are disclosed. It should be apparent to thoseskilled in the art that many additional modifications beside thosealready described are possible without departing from the inventiveconcepts. In interpreting this disclosure, all terms should beinterpreted in the broadest possible manner consistent with the context.Variations of the term “comprising” should be interpreted as referringto elements, components, or steps in a non-exclusive manner, so thereferenced elements, components, or steps may be combined with otherelements, components, or steps that are not expressly referenced.Embodiments referenced as “comprising” certain elements are alsocontemplated as “consisting essentially of” and “consisting of” thoseelements. When two or more ranges for a particular value are recited,this disclosure contemplates all combinations of the upper and lowerbounds of those ranges that are not explicitly recited. For example,recitation of a value of between 1 and 10 or between 2 and 9 alsocontemplates a value of between 1 and 9 or between 2 and 10.

As used herein, the terms “activated” and “activation” refer to T cellsthat are CD3+, CD4+, and/or CD8+.

As used herein, the term “FAD” refers to flavin adenine dinucleotide.

As used herein, the term “memory” includes a non-volatile medium, e.g.,a magnetic media or hard disk, optical storage, or flash memory; avolatile medium, such as system memory, e.g., random access memory (RAM)such as DRAM, SRAM, EDO RAM, RAMBUS RAM, DR DRAM, etc.; or aninstallation medium, such as software media, e.g., a CD-ROM, or floppydisks, on which programs may be stored and/or data communications may bebuffered. The term “memory” may also include other types of memory orcombinations thereof

As used herein, the term “NAD(P)H” refers to reduced nicotinamideadenine dinucleotide and/or reduced nicotinamide dinucleotide phosphate.

As used herein, the term “processor” may include one or more processorsand memories and/or one or more programmable hardware elements. As usedherein, the term “processor” is intended to include any of types ofprocessors, CPUs, microcontrollers, digital signal processors, or otherdevices capable of executing software instructions.

As used herein, the term “redox ratio” or “optical redox ratio” refersto a ratio of NAD(P)H fluorescence intensity to FAD fluorescenceintensity; a ratio of FAD fluorescence intensity to NAD(P)H fluorescenceintensity; a ratio of NAD(P)H fluorescence intensity to any arithmeticcombination including FAD fluorescence intensity; or a ratio of FADfluorescence intensity to any arithmetic combination including NAD(P)Hfluorescence intensity.

Autofluorescence endpoints include photon counts/intensity andfluorescence lifetimes. The fluorescence lifetime of cells can be asingle value, the mean fluorescence lifetime, or compromised from thelifetime values of multiple subspecies with different lifetimes. In thiscase, multiple lifetimes and lifetime component amplitude values areextracted. Both NAD(P)H and FAD can exist in quenched (short lifetime)and unquenched (long lifetime) configurations; therefore, thefluorescence decays of NAD(P)H and FAD are fit to two components.Generally, NADH and FAD fluorescence lifetime decays are fit to a twocomponent exponential decay, I(t)=α₁e^(−t/τ1)+α₂e^(−t/τ2)+C, where I(t)is the fluorescence intensity as a function of time, t, after the laserpulse, α₁ and α₂ are the fractional contributions of the short and longlifetime components, respectively (i.e., α₁+α₂=1), τ₁ and τ₂ are theshort and long lifetime components, respectively, and C accounts forbackground light. However, the lifetime decay can be fit to morecomponents (in theory any number of components, although practically upto ˜5-6) which would allow quantification of additional lifetimes andcomponent amplitudes. By convention lifetimes and amplitudes arenumbered from short to long, but this could be reversed. A mean lifetimecan be computed from the lifetime components, (τ_(m)=α₁τ₁+α₂τ₂ . . . ).Fluorescence lifetimes and lifetime component amplitudes can also beapproximated from frequency domain data and gated cameras/detectors. Forgated detection, α₁ could be approximated by dividing the detectedintensity at early time bins by later time bins. Alternatively,fluorescence anisotropy can be measured by polarization-sensitivedetection of the autofluorescence, thus identifying free NAD(P)H as theshort rotational diffusion time in the range of 100-700 ps.

NAD(P)H α₁ refers to the contribution of free NAD(P)H and is theshortest lifetime that is not dominated (i.e., greater than 50%) byinstrument response and/or scattering. NAD(P)H α₁ is the contributionassociated with NAD(P)H lifetime values from 200-1500 ns, from 200-1000ns, or from 200-600 ns. For clarity, a claim herein including featuresrelated to a “shortest” lifetime cannot be avoided by defining thelifetime values to include a sacrificial shortest lifetime that isdominated by instrument response and/or scattering.

The various aspects may be described herein in terms of variousfunctional components and processing steps. It should be appreciatedthat such components and steps may be realized by any number of hardwarecomponents configured to perform the specified functions.

Methods

This disclosure provides a variety of methods. It should be appreciatedthat various methods are suitable for use with other methods. Similarly,it should be appreciated that various methods are suitable for use withthe systems described elsewhere herein. When a feature of the presentdisclosure is described with respect to a given method, that feature isalso expressly contemplated as being useful for the other methods andsystems described herein, unless the context clearly dictates otherwise.

Referring to FIG. 1, the present disclosure provides a method 100 ofsorting T cells. At process block 102, the method 100 includes receivinga population of T cells having unknown activation status. At processblock 104, the method 100 includes acquiring an autofluorescence dataset for each T cell of the population of T cells. At process block 106,the method 100 includes physically isolating a first portion of thepopulation of T cells from a second portion of the population of T cellsbased on an activation value.

The autofluorescence data set acquired at process block 104 includeslifetime information and can be acquired in a variety of ways, as wouldbe understood by one having ordinary skill in the spectroscopic artswith knowledge of this disclosure and their own knowledge from thefield. For example, the autofluorescence data can be acquired fromfluorescence decay data. As another example, the autofluorescence datacan be acquired by gating a detector (a camera, for instance) to acquiredata at specific times throughout a decay in order to approximate theautofluorescence endpoints described herein. As yet another example, afrequency domain approach can be used to measure lifetime.Alternatively, fluorescence anisotropy can be measured bypolarization-sensitive detection of the autofluorescence, thusidentifying free NAD(P)H as the short rotational diffusion time in therange of 100-700 ps. The specific way in which autofluorescence data isacquired is not intended to be limiting to the scope of the presentinvention, so long as the lifetime information necessary to determinethe autofluorescence endpoints necessary for the methods describedherein can be suitably measured, estimated, or determined in anyfashion.

The physically isolating of process block 106 is in response to anactivation value determined from the acquired autofluorescence data. Ifthe activation value exceeds a predetermined threshold for a given Tcell, then that T cell is placed into the first portion. If theactivation value is less than or equal to the predetermined thresholdfor the given T cell, then that T cell is placed into the secondportion. The result of this physically isolating is that the firstportion of the population of T cells is significantly enriched inactivated T cells, whereas the second portion of the population of Tcells is significantly depleted of activated T cells.

In some cases, the physically isolating of process block 106 can includeisolating cells into three, four, five, six, or more portions. In thesecases, the different portions will be separated by a number ofpredetermined thresholds that is one less than the number of portions(i.e., three portions=two predetermined thresholds). The portion whoseactivation value exceeds all of the predetermined thresholds (i.e.,exceeds the highest threshold) contains the greatest concentration ofactivated T cells. The portion whose activation value fails to exceedany of the predetermined thresholds (i.e., fails to exceed the lowestthreshold) contains the lowest concentration of activated T cells. Usingmultiple predetermined thresholds can afford the preparation of portionsof the population of T cells that have extremely high or extremely lowconcentrations of activated T cells.

The activation value is computed using at least one metabolic endpointof the autofluorescence data set for each T cell of the population of Tcells as an input. The activation value is computed using an equationthat is generated by a machine learning process on data for a populationof T cells having a known activation state using the at least onemetabolic endpoint as a variable. In some cases, the activation valuecan have different predictability for the different kinds of activation(i.e., can be more predictive of CD8+ activation than CD4+ activation orvice versa).

The at least one metabolic endpoint includes the NAD(P)H shortestlifetime amplitude component or NAD(P)H α₁. The at least one metabolicendpoint can also optionally include one or more of the following:NAD(P)H fluorescence intensity; FAD fluorescence intensity; an opticalredox ratio (i.e., a combination of NAD(P)H and FAD intensities such asNAD(P)H/FAD or FAD/NAD(P)H or FAD/[NAD(P)H+FAD] orNAD(P)H/[NAD(P)H+FAD], see definition above); NAD(P)H second shortestlifetime amplitude component or NADPH α₂; NAD(P)H third shortestlifetime amplitude component or NADPH α₃; NAD(P)H mean fluorescencelifetime or NAD(P)H τ_(m); NAD(P)H first fluorescence lifetime orNAD(P)H τ₁; NAD(P)H second fluorescence lifetime or NAD(P)H τ₂; NAD(P)Hthird fluorescence lifetime or NAD(P)H τ₃; NAD(P)H fourth fluorescencelifetime or NAD(P)H τ₄; NAD(P)H fifth fluorescence lifetime or NAD(P)Hτ₅; FAD first amplitude component or FAD α₁; FAD second shortestlifetime amplitude component or FAD α₂; FAD third shortest lifetimeamplitude component or FAD α₃; FAD mean fluorescence lifetime or FADτ_(m); FAD first fluorescence lifetime or FAD τ₁; FAD secondfluorescence lifetime or FAD τ₂; FAD third fluorescence lifetime or FADτ₃; FAD fourth fluorescence lifetime or FAD τ₄; and FAD fifthfluorescence lifetime or FAD τ₅.

In some cases, two, three, four, five, six, seven, eight, nine, ten, ormore inputs are used.

In cases where two inputs are used, those two inputs can be NAD(P)H α₁and cell size. In cases where three inputs are used, those three inputscan be NAD(P)H α₁, cell size, and redox ratio. In cases where fourinputs are used, those four inputs can be NAD(P)H α₁, cell size, redoxratio, and NAD(P)H τ₁. In cases where five or more inputs are used,those five or more inputs can be NAD(P)H τ₁, cell size, redox ratio,NAD(P)H τ₁, and one or more of the above-referenced metabolic endpoints.

When one endpoint is used, the activation value is simply the endpointvalue, which is then compared to the threshold value for the purposes ofthe physically isolating step. When two more endpoints are used, amathematical formula is used to provide the activation value. Thisformula is a multi-variable equation that produces a single activationvalue. The multi-variable equation can be selected, determined, orotherwise calculated using classification and feature selection machinelearning, including but not limited to, linear regression, logisticregression, random forest, support vector machines, neural networks,quadratic regression, k-means clustering, and the like.

The method 100 can sort T cells based on CD4+, CD3+, and/or CD8+activation status.

The method 100 can provide surprising accuracy of classifying T cells asactivated. The accuracy can be at least 75%, at least 80%, at least 85%,at least 90%.

Referring to FIG. 2, the present disclosure provides a method 200 ofadministering activated T cells to a subject in need thereof. At processblock 202, the method 200 includes the method 100 described above, whichresults in a first portion of the population of T cells enriched foractivation. At optional process block 204, the method 200 optionallyincludes modifying the first portion of the population of T cells. Atprocess block 206, the method 200 includes administering the firstportion of the population of T cells to the subject.

The T cells can be harvested from the subject to which they areadministered prior to sorting. The sorted T cells can be either directlyintroduced to the subject or can undergo additional processing prior tointroduction to the subject. In one case, the sorted T cells can bemodified to contain chimeric antigen receptors (CARs).

The methods described herein provided surprising results to theinventors in at least three ways. First, it was not clear at the outsetwhether the methods would be effective at distinguishing activatedversus quiescent T cells, so the efficacy itself was surprising and thequality of the classification achieved by the methods was even moresurprising. Second, the inventors expected different endpoints toprovide the strongest classification results and were surprised when theNAD(P)H shortest lifetime amplitude component (i.e., NAD(P)H α₁) provedto be the strongest classification endpoint. Existing methods emphasizecell size in order to determine activation state, so one might haveexpected cell size to play a more significant part in classificationthan it does in the methods described herein. To be clear, includingcell size as one of the endpoints in the methods described herein doesimprove classification, but the inventors anticipated cell size being alarger contributor to classification accuracy. With respect to thefluorescence endpoints themselves, the inventors anticipated that theredox ratio would be the strongest contributor to classificationaccuracy, but surprisingly discovered that the NAD(P)H shortest lifetimeamplitude component was the strongest contributor. Third, the degree ofclassification accuracy achieved by any single endpoint, let alone thesurprising NAD(P)H shortest lifetime amplitude component, wassurprising. The classification accuracy of upward of 80-90% that isachieved using only the NAD(P)H first amplitude component could not havebeen predicted.

Systems

This disclosure also provides systems. The systems can be suitable foruse with the methods described herein. When a feature of the presentdisclosure is described with respect to a given system, that feature isalso expressly contemplated as being combinable with the other systemsand methods described herein, unless the context clearly dictatesotherwise.

Referring to FIG. 3, the present disclosure provides a T cell sortingdevice 300. The device 300 includes a cell sorting pathway 302. The cellsorting pathway 302 includes an inlet 304, an observation zone 306, anda cell sorter 308. The observation zone 306 is coupled to the inlet 304downstream of the inlet 304. The device 300 also includes a single-cellautofluorescence detector 310. The device 300 includes a processor 312and a non-transitory computer-readable medium 314, such as a memory.

The inlet 304 can be any nanofluidic, microfluidic, or other cellsorting inlet. A person having ordinary skill in the art of fluidics hasknowledge of suitable inlets 304 and the present disclosure is notintended to be bound by one specific implementation of an inlet 304.

The observation zone 306 is configured to present T cells for individualautofluorescence interrogation. A person having ordinary skill in theart has knowledge of suitable observation zones 306 and the presentdisclosure is not intended to be bound by one specific implementation ofan observation zone 306.

The cell sorter 308 has a sorter inlet 316 and at least two sorteroutlets 318. The cell sorter is coupled to the observation zone 306 viathe sorter inlet 316 downstream of the observation zone 306. The cellsorter 308 is configured to selectively direct a cell from the sorterinlet 316 to one of the at least two sorter outlets 318 based on asorter signal.

The inlet 304, observation zone 306, and cell sorter 308 can becomponents known to those having ordinary skill in the art to be usefulin flow sorters, including commercial flow sorters. The cell sortingpathway can further optionally include a flow regulator, as would beunderstood by those having ordinary skill in the art. The flow regulatorcan be configured to provide flow of cells through the observation zoneat a rate that allows the single cell autofluorescence detector 310 toacquire the autofluorescence lifetime information. A useful review ofthe sorts of fluidics that can be used in combination with the presentdisclosure is Shields et al., “Microfluidic cell sorting: a review ofthe advances in the separation of cells from debulking to rare cellisolation,” Lab Chip, 2015 Mar. 7; 15(5): 1230-49, which is incorporatedherein by reference in its entirety.

The single-cell autofluorescence detector 310 can be any detectorsuitable for measuring single-cell autofluorescence as understood bythose having ordinary skill in the optical arts. Examples of suitablesingle-cell autofluorescence detectors 310 include, but are not limitedto, a photomultiplier tube, a camera, a photodiode, an avalanchephotodiode, a streak camera, a charge capture device, and the like.

The single-cell autofluorescence detector 310 can be directly (i.e., theprocessor 312 communicates directly with the detector 310 and receivesthe signals) or indirectly (i.e., the processor 312 communicates with asub-controller that is specific to the detector 310 and the signals fromthe detector 310 can be modified or unmodified before sending to theprocessor 312) controlled by the processor 312. Fluorescence lifetimeinformation can be obtained using time-domain (time-correlatedsingle-photon counting, gated detection) or frequency-domain methods.The device 300 can include various optical filters tuned to isolateautofluorescence signals of interest. The optical filters can be tunedto the autofluorescence wavelengths of NAD(P)H and/or FAD.

The device 300 can optionally include a light source 320 for opticallyexciting the cells to initiate autofluorescence. Suitable light sources320 include, but are not limited to, lasers, LEDs, lamps, filteredlight, fiber lasers, and the like. The light source 320 can be pulsed,which includes sources that are naturally pulsed and continuous sourcesthat are chopped or otherwise optically modulated with an externalcomponent. The light source 320 can provide pulses of light having afull-width at half maximum (FWHM) pulse width of between 1 fs and 10 ns.In some cases, the FWHM pulse width is between 30 fs and 1 ns. The lightsource 320 can emit wavelengths that are tuned to the absorption ofNAD(P)H and/or FAD.

The single-cell autofluorescence detector 310 can be configured toacquire the autofluorescence data set at a repetition rate of between 1kHz and 20 GHz. In some cases, the repetition rate can be between 1 MHzand 1 GHz. In other cases, the repetition rate can be between 20 MHz and100 MHz. The light source 320 can be configured to operate at theserepetition rates.

The device 300 can optionally include a cell size measurement tool 322.The cell size measurement tool 322 can be any device capable ofmeasuring the size of cells, including but not limited to, an opticalmicroscope. In some cases, the single-cell autofluorescence detector 310and the cell size measurement tool 322 can be integrated into a singleoptical subsystem.

The processor 312 is in electronic communication with the detector 310and the cell sorter 308. The processor 312 is also in electroniccommunication with, when present, the optional light source 320 and theoptional cell size measurement tool 322.

The non-transitory computer-readable medium 314 has stored thereoninstructions that, when executed by the processor, cause the processorto execute at least a portion of the methods described herein.

EXAMPLE 1

Peripheral blood was drawn from healthy donors into sterile syringescontaining heparin. CD3+, CD4+, or CD8+ T cells were extracted fromwhole blood (RosetteSep, StemCell Technologies) and cultured in T cellactivation media (StemCell Technologies). Twenty-four hourspost-isolation, a tetrameric antibody for CD2/CD3/CD28 was added to theculture media to activate the T cells. Activation state (CD69+) and Tcell subtype (CD4+, CD8+) was verified with antibody immunofluorescence.Fluorescence intensity and lifetime images were acquired using acustom-built multiphoton fluorescence microscope (Bruker FluorescenceMicroscopy, Middleton, Wis.) adapted for fluorescence lifetime imagingwith time-correlated single photon counting electronics. T cells wereimaged with a 100× objective (NA=1.3). A tunable titanium:sapphire laserprovided the excitation light at 750 nm for NAD(P)H excitation and 890nm for FAD excitation. The laser power at the sample was 3.0-3.5 mW forNAD(P)H and 5.3-5.7 mW for FAD. A bandpass filters, 440/80 nm and550/100 nm, were used to filter NAD(P)H and FAD fluorescence emission,respectively. Fluorescence emission was detected by GaAsP PMTs, andfluorescence lifetime decays with 256 time bins were acquired for eachpixel by time correlated single photon counting electronics (SPC-150,Becker & Hickl, Berlin, Germany). The second harmonic generation fromred blood cells was used as the instrument response function and had afull width at half maximum of 240 ps. Fluorescence lifetime decays weredeconvolved from the instrument response function and fit to a 2component exponential decay model, I(t)=α₁*exp(−t/τ₁)+α₂*exp(−t/τ₂)+C,where I(t) is the fluorescence intensity as a function of time, t, afterthe laser pulse, α₁ and α₂ are the fractional contributions of the shortand long lifetime components, respectively (i.e., α₁+α₂=1), τ₁ and τ₂are the short and long lifetimes, respectively, and C accounts forbackground light. NAD(P)H intensity images were segmented intocytoplasm, mitochondria, and nucleus using edge detect and thresholdingmethods in CellProfiler using a customized image processing routine.Images of the optical redox ratio (fluorescence intensity of NAD(P)Hdivided by the summed intensity of NAD(P)H and FAD) and meanfluorescence lifetime (τ_(m)=α₁*τ₁+α₂*τ₂) of NAD(P)H and FAD werecomputed (Matlab). OMI endpoints, including the optical redox ratio,NAD(P)H τ_(m), NAD(P)H τ₁, NAD(P)H τ₂, NAD(P)H α₁, FAD τ_(m), FAD τ₁,FAD τ₂, and FAD τ₁ were averaged across all pixels within a cell foreach segmented cell. Cell size in μm² was also computed from the numberof pixels within the cell.

The results show a significant increase (p<0.001) in cell size, opticalredox ratio, NAD(P)H α₁, FAD α₁, and NAD(P)H τ₁ between quiescent andactivated CD3+ and CD8+ T cells across 6 donors. NAD(P)H τ_(m), and FADτ₁ were significantly (p<0.001) decreased in activated CD3+ and CD8+ Tcells as compared to quiescent T cells. FAD τ_(m) was significantlydecreased in activated CD3+ T cells as compared with quiescent CD3+ Tcells, but no significant difference in FAD τ_(m) was observed betweenquiescent and activated CD8+ T cells. NAD(P)H τ₂ was significantlyincreased in activated CD3+ T cells as compared to quiescent CD3+ Tcells and significantly decreased in activated CD8+ T cells as comparedto quiescent CD8+ T cells. FAD τ₂ was significantly increased inactivated CD3+ T cells as compared to quiescent CD3+ T cells, and nosignificant difference was observed in FAD τ₂ between quiescent andactivated CD8+ T cells. Similar results were obtained for T cellscultured with the activation antibodies for 24 hours. Consistent resultswere obtained from a repeat experiment on the T cells of one donor

The CD3+ and CD8+ quiescent and activated data sets were divided intotraining and testing data for classification: cells from 4 donors knownto be quiescent or activated by culture conditions were used to trainthe models (n=4131 CD3+ cells, 2655 CD8+ cells) and cells from 3 donorswith same-cell CD69 validation of activation state were used to test themodels (n=696 CD3+ cells, 595 CD8+ cells). Gain ratio, random forest,and Chi-squared methods for weighting features revealed NAD(P)H α₁ asthe most important feature for classification of activation state ofCD3+ and CD8+ T cells. For CD3+ T cells, the gain ratio determined orderof importance of features was NAD(P)H α₁, cell size, redox ratio,NAD(P)H τ₁, FAD α₁, NAD(P)H τ_(m), FAD τ₂, FAD τ₁, NAD(P)H τ₂, and FADτ_(m). For CD8+ T cells, the gain ratio determined order of importanceof features was NAD(P)H α₁, redox ratio, NAD(P)H τ_(m), cell size,NAD(P)H τ₂, FAD τ₁, FAD τ_(m), FAD τ₂, NAD(P)H τ₁, and FAD α₁. Alogistic regression classification model, yielded accuracies of 97.5%,70.8%, 96.5%, 90.1%, 97.1%, and 93.9% for the classification of CD3+ Tcells as activated or quiescent when trained on all 10 features; cellsize only; NAD(P)H α₁ only; redox ratio and cell size; redox ratio, cellsize and NAD(P)H α₁; and redox ratio, NAD(P)H τ_(m) and NAD(P)H α₁. Alogistic regression classification model yielded accuracies of 99.6%,68.7%, 99.4%, 95.7%, 99.9%, and 98.4% for the classification of CD8+ Tcells as activated or quiescent when trained on all 10 features; cellsize only; NAD(P)H α₁ only; redox ratio and cell size; redox ratio, cellsize and NAD(P)H α₁; and redox ratio, NAD(P)H τ_(m) and NAD(P)H α₁.Similar accuracies (˜94-99%) were achieved with different classificationmodels including random forest and support vector machines when trainedwith all 10 features. No improvement in classification accuracy wasachieved by training and testing with donor normalized data.

All prior results were for quiescent and activated T cells grown inisolated dishes. To ensure this method is robust at distinguishingactivated from not activated T cells in mixed populations, we evaluatedquiescent and activated T cells that were cultured separately for 48hours and then mixed into co-culture one hour before imaging. Mixedco-culture of quiescent and activated T cells causes some changes in theautofluorescence endpoints. For example, the redox ratio wassignificantly decreased in activated CD3+ T cells in mixed co-culturesof activated and quiescent T cells as compared to that of activated CD3+T cells cultured without quiescent T cells. NAD(P)H α₁ was significantlyincreased in activated CD3+ T cells in mixed cultures of activated andquiescent T cells and remained the most important feature (˜0.35normalized weight) for classification, as determined by random forestfeature selection methods. NAD(P)H τ_(m) and NAD(P)H τ₂ were the secondand third most important features with normalized weights of ˜0.22 and˜0.1. Logistic regression classification of quiescent and activated Tcells within mixed cultures yielded accuracies of 95.5%, 76.6%, 100%,and 72.7% when trained on all 10 features, cell size only, NAD(P)H α₁only, and redox ratio and cell size, respectively.

The present disclosure also includes the following statements:

-   1. A T cell sorting device comprising:

a cell sorting pathway comprising:

-   -   (i) an inlet;    -   (ii) an observation zone coupled to the inlet downstream of the        inlet, the observation zone configured to present T cells for        individual autofluorescence interrogation; and    -   (iii) a cell sorter having a sorter inlet and at least two        sorter outlets, the cell sorter coupled to the observation zone        via the sorter inlet downstream of the observation zone, the        cell sorter configured to selectively direct a cell from the        sorter inlet to one of the at least two sorter outlets based on        a sorter signal;

a single-cell autofluorescence detector configured to acquire anautofluorescence data set from a T cell positioned in the observationzone, the single-cell autofluorescence detector configured to acquireautofluorescence lifetime information for each autofluorescence dataset;

a processor in electronic communication with the cell sorter and thesingle-cell autofluorescence detector; and

a non-transitory computer-readable medium having stored thereoninstructions that, when executed by the processor, cause the processorto:

-   -   a) receive the autofluorescence data set; and    -   b) provide the sorter signal to the cell sorter based on an        activation value, wherein the sorter signal directs the cell        sorter to selectively direct the T cell to a first outlet of the        at least two sorter outlets when the activation value exceeds a        predetermined threshold and to a second outlet of the at least        two sorter outlets when the activation value is less than or        equal to the predetermined threshold, wherein the activation        value is computed using at least one metabolic endpoint of the        autofluorescence data set for the T cell as an input, wherein        the at least one metabolic endpoint includes reduced        nicotinamide adenine dinucleotide (NAD(P)H) shortest        fluorescence lifetime amplitude component (α₁).

-   2. The T cell sorting device of statement 1, wherein the cell    sorting pathway comprises a microfluidic pathway or a nanofluidic    pathway.

-   3. The T cell sorting device of statement 1 or 2, the T cell sorting    device further comprising a flow regulator coupled to the inlet.

-   4. The T cell sorting device of any one of the preceding statements,    wherein the flow regulator is configured to provide flow of cells    through the observation zone at a rate that allows the single-cell    autofluorescence spectrometer to acquire the autofluorescence    lifetime information for each autofluorescence data set.

-   5. The T cell sorting device of any one of the preceding statements,    the T cell sorting device further comprising a light source.

-   6. The T cell sorting device of any one of the preceding statements,    wherein the light source is a pulsed light source.

-   7. The T cell sorting device of any one of the preceding statements,    wherein the pulsed light source is a Ti:sapphire laser.

-   8. The T cell sorting device of any one of the preceding statements,    wherein the light source emits light having a wavelength tuned to    excite fluorescence from NAD(P)H.

-   9. The T cell sorting device of any one of the preceding statements,    the single-cell autofluorescence spectrometer comprising a pulsed    light source having a full width at half maximum pulse width of    between 1 fs and 10 ns.

-   10. The T cell sorting device of any one of the preceding    statements, the single-cell autofluorescence spectrometer comprising    a pulsed light source having a full width at half maximum pulse    width of between 30 fs and 1 ns.

-   11. The T cell sorting device of any one of the preceding    statements, wherein the single-cell autofluorescence detector is    configured to acquire the autofluorescence data set at a repetition    rate of between 1 kHz and 20 GHz.

-   12. The T cell sorting device of any one of the preceding    statements, wherein the single-cell autofluorescence detector is    configured to acquire the autofluorescence data set at a repetition    rate of between 1 MHz and 1 GHz.

-   13. The T cell sorting device of any one of the preceding    statements, wherein the single-cell autofluorescence detector is    configured to acquire the autofluorescence data set at a repetition    rate of 20 MHz and 100 MHz.

-   14. The T cell sorting device of any one of the preceding    statements, wherein the single-cell autofluorescence detector is    configured to acquire the autofluorescence data set via    time-correlated single photon counting.

-   15. The T cell sorting device of any one of the preceding    statements, wherein the single-cell autofluorescence detector is a    photomultiplier tube or a photodiode.

-   16. The T cell sorting device of any one of the preceding    statements, the single-cell autofluorescence detector comprising a    detector-side filter configured to transmit fluorescence signals of    interest.

-   17. The T cell sorting device of the immediately preceding    statement, wherein the detector-side filter is configured to    transmit NAD(P)H fluorescence.

-   18. The T cell sorting device of any one of the preceding    statements, the T cell sorting device further comprising a cell size    measurement tool configured to measure cell size and to communicate    the cell size to the processor.

-   19. The T cell sorting device of any one of the preceding    statements, the T cell sorting device further comprising a cell    imager configured to acquire an image of a cell positioned within    the observation zone and to communicate the image to the processor.

-   20. A method of characterizing T cell activation state, the method    comprising:

a) optionally receiving a population of T cells having unknownactivation status;

b) acquiring an autofluorescence data set for a T cell of the populationof T cells, the autofluorescence data set optionally includingautofluorescence lifetime information;

c) identifying an activation status of the T cell based on an activationvalue, wherein the activation value is computed using at least a portionof the autofluorescence data set, wherein the activation value isoptionally computed using at least one metabolic endpoint of theautofluorescence data set for the T cell as an input, wherein the atleast one metabolic endpoint optionally includes NAD(P)H α₁.

-   21. A method of sorting T cells, the method comprising:

a) receiving a population of T cells having unknown activation status;

b) acquiring an autofluorescence data set for each T cell of thepopulation of T cells, each autofluorescence data set includingautofluorescence lifetime information; and

c) physically isolating a first portion of the population of T cellsfrom a second portion of the population of T cells based on anactivation value, wherein each T cell of the population of T cells isplaced into the first portion when the activation value exceeds apredetermined threshold and into the second portion when the activationvalue is less than or equal to the predetermined threshold, wherein theactivation value is computed using at least one metabolic endpoint ofthe autofluorescence data set for each T cell of the population of Tcells as an input, wherein the at least one metabolic endpoint includesNAD(P)H α₁.

-   22. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    further includes an endpoint selected from the group consisting of    NAD(P)H fluorescence intensity, FAD fluorescence intensity, optical    redox ratio defined as NAD(P)H/FAD or FAD/NAD(P)H or    NAD(P)H/[FAD+NAD(P)H] or FAD/[FAD+NAD(P)H], NAD(P)H mean    fluorescence lifetime (τ_(m)), NAD(P)H first fluorescence lifetime    component (τ₁), NAD(P)H second fluorescence lifetime component (τ₂),    flavin adenine dinucleotide (FAD) τ_(m), FAD α₁, FAD τ₁, FAD τ₂, and    combinations thereof.-   23. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes the optical redox ratio.-   24. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes NAD(P)H τ₁.-   25. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes NAD(P)H τ_(m).-   26. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes NAD(P)H τ₂.-   27. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes FAD τ_(m).-   28. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes FAD α₁.-   29. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes FAD τ₁.-   30. The T cell sorting device or the method of any one of the    preceding statements, wherein the at least one metabolic endpoint    includes FAD τ₂.-   31. The T cell sorting device or the method of any one of the    preceding statements, wherein the activation value is computed using    cell size for each T cell of the population of T cells as an input.-   32. The T cell sorting device or the method of any one of the    preceding statements, wherein the predetermined threshold is    selected via classification and feature selection machine learning    on a control population of T cells having known activation states.-   33. The T cell sorting device or the method of any one of the    preceding statements, wherein the predetermined threshold is    donor-normalized.-   34. The T cell sorting device or the method of any one of the    preceding statements, wherein the T cells whose activation value    exceeds the predetermined threshold are CD3+, CD4+ or CD8+ T cells.-   35. The T cell sorting device or the method of any one of the    preceding statements, wherein an accuracy of classifying T cells as    activated is at least 75%.-   36. The T cell sorting device or the method of the immediately    preceding statement, wherein the accuracy of classifying T cells as    activated is at least 80%.-   37. The T cell sorting device or the method of the immediately    preceding statement, wherein the accuracy of classifying T cells as    activated is at least 90%.-   38. The T cell sorting device or the method of the immediately    preceding statement, wherein the accuracy of classifying T cells as    activated is at least 95%.-   39. A method of administering activated T cells to a subject in need    thereof, the method comprising:

a) the method of any one of statements 21 to the immediately precedingstatement; and

b) introducing the first portion of the population of T cells to thesubject.

-   40. The method of statement 39, wherein the first portion of the    population of T cells is modified prior to step b).-   41. The method of statement 40, wherein the first portion of the    population of T cells is modified to include a chimeric antigen    receptor prior to step b). We claim:

1. A T cell sorting device comprising: a cell sorting pathwaycomprising: (i) an inlet; (ii) an observation zone coupled to the inletdownstream of the inlet, the observation zone configured to present Tcells for individual autofluorescence interrogation; and (iii) a cellsorter having a sorter inlet and at least two sorter outlets, the cellsorter coupled to the observation zone via the sorter inlet downstreamof the observation zone, the cell sorter configured to selectivelydirect a cell from the sorter inlet to one of the at least two sorteroutlets based on a sorter signal; a single-cell autofluorescencedetector configured to acquire an autofluorescence data set from a Tcell positioned in the observation zone, the single-cellautofluorescence detector configured to acquire autofluorescencelifetime information for each autofluorescence data set; a processor inelectronic communication with the cell sorter and the single-cellautofluorescence detector; and a non-transitory computer-readable mediumhaving stored thereon instructions that, when executed by the processor,cause the processor to: a) receive the autofluorescence data set; and b)provide the sorter signal to the cell sorter based on an activationvalue, wherein the sorter signal directs the cell sorter to selectivelydirect the T cell to a first outlet of the at least two sorter outletswhen the activation value exceeds a predetermined threshold and to asecond outlet of the at least two sorter outlets when the activationvalue is less than or equal to the predetermined threshold, wherein theactivation value is computed using at least one metabolic endpoint ofthe autofluorescence data set for the T cell as an input, wherein the atleast one metabolic endpoint includes reduced nicotinamide adeninedinucleotide (NAD(P)H) shortest fluorescence lifetime amplitudecomponent (α₁).
 2. The T cell sorting device of claim 1, wherein thecell sorting pathway comprises a microfluidic pathway or a nanofluidicpathway.
 3. The T cell sorting device of claim 1, the T cell sortingdevice further comprising a light source.
 4. The T cell sorting deviceof claim 1, the T cell sorting device further comprising a pulsed lightsource having a full width at half maximum pulse width of between 1 fsand 10 ns.
 5. The T cell sorting device of claim 1, wherein thesingle-cell autofluorescence detector is configured to acquire theautofluorescence data set at a repetition rate of between 1 kHz and 20GHz.
 6. The T cell sorting device of claim 1, wherein the single-cellautofluorescence detector is configured to acquire the autofluorescencedata set via time-correlated single photon counting.
 7. The T cellsorting device of claim 1, wherein the single-cell autofluorescencedetector is a photomultiplier tube or a photodiode.
 8. The T cellsorting device of claim 1, the T cell sorting device further comprisinga cell size measurement tool configured to measure cell size and tocommunicate the cell size to the processor.
 9. The T cell sorting deviceof claim 8, wherein the activation value is computed using the cell sizefor each T cell of the population of T cells as an input.
 10. The T cellsorting device of claim 1, wherein the at least one metabolic endpointfurther includes an endpoint selected from the group consisting ofoptical redox ratio, NAD(P)H mean fluorescence lifetime (τ_(m)), NAD(P)Hfirst fluorescence lifetime component (τ₁), NAD(P)H second fluorescencelifetime component (τ₂), flavin adenine dinucleotide (FAD) τ_(m), FADα₁, FAD τ₁, FAD τ₂, and combinations thereof.
 11. A method of sorting Tcells, the method comprising: a) receiving a population of T cellshaving unknown activation status; b) acquiring an autofluorescence dataset for each T cell of the population of T cells, each autofluorescencedata set including autofluorescence lifetime information; and c)physically isolating a first portion of the population of T cells from asecond portion of the population of T cells based on an activationvalue, wherein each T cell of the population of T cells is placed intothe first portion when the activation value exceeds a predeterminedthreshold and into the second portion when the activation value is lessthan or equal to the predetermined threshold, wherein the activationvalue is computed using at least one metabolic endpoint of theautofluorescence data set for each T cell of the population of T cellsas an input, wherein the at least one metabolic endpoint includesreduced nicotinamide adenine dinucleotide (NAD(P)H) shortestfluorescence lifetime amplitude component (α₁).
 12. The method of claim11, wherein the at least one metabolic endpoint further includes anendpoint selected from the group consisting of NAD(P)H fluorescenceintensity, FAD fluorescence intensity, optical redox ratio, NAD(P)H meanfluorescence lifetime (τ_(m)), NAD(P)H first fluorescence lifetimecomponent (τ₁), NAD(P)H second fluorescence lifetime component (τ₂),flavin adenine dinucleotide (FAD) τ_(m), FAD α₁, FAD τ₁, FAD τ₂, andcombinations thereof.
 13. The method of claim 11, wherein the activationvalue is computed using cell size for each T cell of the population of Tcells as an input.
 14. The method of claim 11, wherein the predeterminedthreshold is selected via classification and feature selection machinelearning on a control population of T cells having known activationstates.
 15. The method of claim 11, wherein the predetermined thresholdis donor-normalized.
 16. The method of claim 11, wherein the T cellswhose activation value exceeds the predetermined threshold are CD3+,CD4+, or CD8+ T cells.
 17. The method of claim 11, wherein an accuracyof classifying T cells as activated is at least 75%.
 18. A method ofadministering activated T cells to a subject in need thereof, the methodcomprising: a) the method of claim 11; and b) introducing the firstportion of the population of T cells to the subject.
 19. The method ofclaim 18, wherein the first portion of the population of T cells ismodified prior to step b).
 20. The method of claim 19, wherein the firstportion of the population of T cells is modified to include a chimericantigen receptor prior to step b).